ArticleJAMIA open2025
Exploring common data model coverage of nursing flowsheet data: a pilot study using SNOMED CT and LOINC mapping.
Article in JAMIA open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: The primary objective of this research is to assess the content coverage of nursing data within a publicly available common data model (CDM), focusing on how nursing data, documented in flowsheets, are represented within the model. Materials and Methods: This mapping study was informed by previous evaluation studies and serves as a framework for evaluating information resources, including to guide development and implementation. The overall research process consists of 4 steps: (1) identify a CDM; (2) define evaluation criteria; (3) map nursing flowsheet data; and (4) apply evaluation criteria. Results: Overall, 65.5% ( Discussion: This mapping study demonstrated the progress and ongoing challenges of mapping nursing data to a national common data model. However, the ability to use nursing data at scale in a national CDM remains limited until more comprehensive mapping is completed. Conclusion: This mapping study identifies a significant gap in integrating nursing data into a national common data model, highlighting an opportunity to enhance patient care through improved real-time insights and evidence-based nursing practices. Addressing this gap can help shape policies that prioritize the inclusion of nursing data. Additionally, aligning nursing data at scale can advance research, increase efficiency, and optimize nurse-sensitive patient outcomes.
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